The present invention relates to a process for estimating a dominant motion between two successive frames of a video sequence. This process is valid for all kind of scene or camera motion configuration.
The estimation of the dominant motion is an important step for estimating or segmenting the apparent motion between two successive frames. In a dense motion field estimation, it strongly accelerates the process by providing an initial motion near the true apparent motion over a large part of the frame. This is especially the case when the dominant motion is not constant, that is not a pure translational apparent motion, but more complicated such as a zoom or a rotation around the optical axis of the camera. In a motion segmentation process, it represents a basic step for identifying the motion of a region, since it may be applied to the entire frame as well as regions of the frame.
The main commonly used approach for estimating the dominant motion between two successive frames is based on a robust regression method. It will be called in the following xe2x80x98robust regression global motion estimationxe2x80x99. The main characteristics of this algorithm are the following:
it uses a global motion model for representing the dominant motion with very few parameters. In most of cases, an affine motion model, which is a good compromise between the physical reality of the apparent motion and the computational complexity, is chosen. It may represent with a good approximation apparent motions resulting from camera motions such as traveling, pan, tilt, zoom, and any combination of these motions.
the estimation of the parameters of this model is performed using a robust regression algorithm which theoretically allows the elimination of outliers, i.e. pixels having a different motion from the dominant motion, from the estimation process.
this process may work without a prior dense motion field estimation. Data used for the global motion estimation process are the source frames. The process is based on the estimated spatio-temporal gradients of the luminance function at each pixel, through the well-known xe2x80x98optic flow constraintxe2x80x99.
this process may be achieved in a multi-resolution scheme, in order to deal with large motion amplitudes.
In many cases this type of algorithms provides satisfactory results, and is able to identify the dominant motion. However the process strongly depends on the initial value of the global motion. In general this global motion is set initially to zero. Moreover, there are many configurations where the estimation process fails and provides a wrong global motion. If the source frames are poorly textured and contain large uniform areas, the process generally converges toward a small amplitude motion, even if the real dominant motion is large. This phenomena is especially true when a large foreground object, tracked by the camera, moves over a background scene.
This problem can be easily explained: as said before, the xe2x80x98motionxe2x80x99 observations are based on the spatio-temporal gradients ; these observations are obviously very few informative on uniform or poorly textured areas. If such areas are majoritory in the frame, the algorithm cannot converge to a right motion value and it considers that a small motion well fits to the motion observations. It is an object of the present invention to solve the main limitations identified above.
The invention relates to a process for estimating the dominant motion between two frames. It combines a phase correlation peak detection algorithm and a multi-resolution robust regression algorithm for improving the robustness of the estimation.